scbrown86

m.stat <- function(mod, obs, ...) {
  # model field = x; observed field = y
  # mse the mean square error between x and y,
  # V and G are spatial variance and domain mean of the respective fields
  # https://doi.org/10.1002/(SICI)1097-0088(199604)16:4%3C379::AID-JOC18%3E3.0.CO;2-U
  obs = na.omit(obs)
  mod = na.omit(mod)
  stopifnot(length(obs) == length(mod))
  se = (obs - mod)^2
  mse = mean(se)

scbrown86

cd /mnt/c/Users/Stu/Desktop/CMIP5/

#CCSM4
cd CCSM4
cdo -ensmean tas_Amon_CCSM4_rcp26_r* CCSM4_rcp26_ensAvg.nc #6 seconds

#CSIRO
cd ../CSIRO-Mk3.6.0/
cdo -ensmean tas_Amon_CSIRO-Mk3-6-0_rcp26_r* CSIRO-Mk3-6-0_rcp26_ensAvg.nc #2.5 seconds

scbrown86

library(tidyverse)
library(scales)
data(diamonds)

diamonds %>% 
  filter(str_detect(cut, "Fair|Ideal")) %>% 
  ggplot(aes(price, carat)) +
  geom_point(color = "skyblue", alpha = 0.5) +
  facet_wrap(~cut, strip.position = "bottom") + 
  scale_x_continuous(labels = comma) +

chris-sev

# how to run this thingy
# create a file on your mac called setup.sh
# run it from terminal with: sh setup.sh

# heavily inspired by https://twitter.com/damcclean
# https://github.com/damcclean/dotfiles/blob/master/install.sh

# faster dock hiding/showing (run in terminal)
# defaults write com.apple.dock autohide-delay -float 0; defaults write com.apple.dock autohide-time-modifier -int 0;killall Dock

pb111

scbrown86

## Function for returning weighted centroid location
## currently hard-coded to return mean, q10, q20, q80 & q90.
weightedCentre <- function(x, y, z) {
  require(matrixStats); require(Hmisc)
  if (anyNA(c(x, y, z))) {
    stop("There are missing values present in x, y, or z")
  }
  if (length(z) != length(x)) {
    stop("Number of weights supplied not equal to number of coordinates")
  }

pb111

hakimabdi

#####################################################################################################
# title         : Machine learning exercise for Sentinel-2 data
# purpose       : Implementing a machine learning workflow in R 
# author        : Abdulhakim M. Abdi (Twitter: @HakimAbdi / www.hakimabdi.com)
# input         : A multi-temporal raster stack of Sentinel-2 data comprising scenes from four dates 
# output        : One classified land cover map from each of three machine learning algorithms  
# Note 1        : This brief tutorial assumes that you are already well-grounded in R concepts and are 
#               : familiar with image classification procedure and terminology
# Reference		  : Please cite Abdi (2020): "Land cover and land use classification performance of machine learning 
#				        : algorithms in a boreal landscape using Sentinel-2 data" in GIScience & Remote Sensing if you find this 

scbrown86

# Use the Cumulative Relative Frequency Curve hotspot method
# to identify hotspots of high values

# Ref:
## Bartolino, V., Maiorano, L., & Colloca, F. (2011).
## A frequency distribution approach to hotspot identification.
## Population Ecology, 53(2), 351-359. doi:10.1007/s10144-010-0229-2


library(raster)

scbrown86

# Use the Cumulative Relative Frequency Curve hotspot method
# to identify hotspots of high values

# Ref:
## Bartolino, V., Maiorano, L., & Colloca, F. (2011).
## A frequency distribution approach to hotspot identification.
## Population Ecology, 53(2), 351-359. doi:10.1007/s10144-010-0229-2

# example array data
# could do with raster/terra, but wanted example in base r